Statistics 154 / 254: Statistical machine learning

UC Berkeley, Fall 2026

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Instructors

Instructor: Ryan Giordano
Ryan Giordano
Office: 389 Evans Hall
Office hours:
Tues + Wed 10:10am-11am
Gateway-LL-Golden Quarter B1040
rgiordano@berkeley.edu
pronouns: He / him

GSI: Lucas Schwengber
Lucas Schwengber
Office: Gateway desk 3330-05
Office hours: 9-11 AM Thu/Fri
Gateway-LL-Classroom B1008 (29)
lucas.schwengber@berkeley.edu
pronouns: He / him

GSI: Josh Davis
Lucas Schwengber
Office: TBD
Office hours: TBD
joshdavis@berkeley.edu
pronouns: He / him

Important

If you are a concurrent enrollment (CE) student hoping to enroll in the class, I hope you will be able to join us! Berkeley policies admit CE students only after the Berkeley add / drop deadline. The add / drop deadline for this year is marked on the course schedule.

If you would like to join the class, please join BCourses and hand in your assignments as if you were enrolled. We will not retoractively waive assignments that were due prior to being enrolled in the class. Preference will be given to students who are demonstrably participating in the class. More details will be provided on ED and during the first lecture.

Please reach out to the GSIs to be manually added to BCourses.

This website will contain lecture materials and assignments. Day-to-day announcements and discussion will be found in ED. (See links above.)

Materials

The course will be based largely on Leture notes. The notes can be supplemented using subsets of the following texts:

Additional reading will supplement these texts as necessary.

Some other good sources of reading material are:

Schedule

Lectures will be held Aug 27th 2026 – Dec 3rd 2026 from 3:30pm – 5pm (starting at Berkeley time, 3:40pm) in Valley Life Sciences 2060.

Labs will be held on Mondays from

  • 9am–10am (Gateway B1026)
  • 4pm–6pm (Gateway B1012)
  • 11am–1pm (Gateway B1023)
  • 1pm-3pm (Gateway B1023)

The official course catalog entry is the ground truth for scheduling. If the catalog conflicts with this webage, please notify the instructor and trust the catalog.

The tentative week-by-week course calendar is as follows. The following schedule is aspirational and subject to change as we go.

According to the official schedule, the final exam has not been scheduled yet .

Calendar (tentative)
Date Day Note Unit Topic Assignment
Aug 27 Thursday Lecture Course policies
Aug 31 Monday Lab Python review
Sep 1 Tuesday Lecture Unit 0: Introduction and review
Sep 3 Thursday Lecture HW 0
Sep 7 Monday Administrative holiday
Sep 8 Tuesday Lecture Unit 1: Regression
Sep 10 Thursday Lecture Quiz 0
Sep 14 Monday Lab Challenge 1
Sep 15 Tuesday Lecture
Sep 16 Wednesday Add / drop deadline
Sep 17 Thursday Lecture
Sep 21 Monday Lab Challenge 1
Sep 22 Tuesday Lecture Unit 2: Classification
Sep 24 Thursday Lecture HW 1
Sep 28 Monday Lab Challenge 1
Sep 29 Tuesday Lecture
Oct 1 Thursday Lecture Quiz 1
Oct 5 Monday Lab Reading 1
Oct 6 Tuesday Lecture Unit 3: Risk and Complexity
Oct 8 Thursday Lecture HW 2
Oct 12 Monday Lab Challenge 2
Oct 13 Tuesday Lecture
Oct 15 Thursday Lecture Quiz 2
Oct 19 Monday Lab Challenge 2
Oct 20 Tuesday Lecture
Oct 22 Thursday Lecture
Oct 26 Monday Lab Challenge 2
Oct 27 Tuesday Lecture Unit 4: Trees and weak learners
Oct 29 Thursday Lecture HW 3
Nov 2 Monday Lab Reading 2
Nov 3 Tuesday Lecture
Nov 5 Thursday Lecture Quiz 3
Nov 9 Monday Lab Challenge 3
Nov 10 Tuesday Lecture
Nov 11 Wednesday Administrative holiday
Nov 12 Thursday Lecture
Nov 16 Monday Lab Challenge 3
Nov 17 Tuesday Lecture Unit 5: Neural networks and optimization
Nov 19 Thursday Lecture HW 4
Nov 23 Monday Lab Challenge 3
Nov 24 Tuesday Lecture
Nov 25 Wednesday Thanksgiving
Nov 26 Thursday Thanksgiving
Nov 27 Friday Thanksgiving
Nov 30 Monday Lab Reading 3
Dec 1 Tuesday Lecture Quiz 4
Dec 3 Thursday Lecture Class review HW 5